Genetics. Sowing the seeds of centromeres.

The centromere is a chromatin-based platform that accumulates microtubule-binding proteins that drive chromosome segregation during cell division. Despite their size (on the order of megabases of DNA in mammals) and conserved role, centromeres have the remarkable capacity to leave their usual comfort zone and to reform at a new chromosomal site (1). Although found rarely, these so-called neocentromeres are by most measures bona fide and segregate chromosomes with high fidelity. What accounts for this nomadic behavior

How two become one: HJURP dimerization drives CENP-A assembly

CENP‐A containing nucleosomes epigenetically specify centromere position on chromosomes. Deposition of CENP‐A into chromatin is mediated by HJURP, a specific CENP‐A chaperone. Paradoxically, HJURP binding sterically prevents dimerization of CENP‐A, which is critical to form functional centromeric nucleosomes. A recent publication in The EMBO Journal (Zasadzińska et al, 2013) demonstrates that HJURP itself dimerizes through a C‐terminal repeat region, which is essential for centromeric assembly of nascent CENP‐A.FCT fellowship: (SFRH/BD/74284/2010); FCT grants: (BIA-BCM/100557/2008, BIAPRO/100537/2008); EMBO Installation Grant

Two-Dimensional Spectroscopy of Extended Molecular Systems: Applications to Energy Transport and Relaxation in an α-Helix

A simulation study of the coupled dynamics of amide I and amide II vibrations in an α-helix dissolved in water shows that two-dimensional (2D) infrared spectroscopy may be used to disentangle the energy transport along the helix through each of these modes from the energy relaxation between them. Time scales for both types of processes are obtained. Using polarization-dependent 2D spectroscopy is an important ingredient in the method we propose. The method may also be applied to other two-band systems, both in the infrared (collective vibrations) and the visible (excitons) parts of the spectrum.

Assembly in G1 phase and long-term stability are unique intrinsic features of CENP-A nucleosomes

Centromeres are the site of kinetochore formation during mitosis. Centromere protein A (CENP-A), the centromere-specific histone H3 variant, is essential for the epigenetic maintenance of centromere position. Previously we showed that newly synthesized CENP-A is targeted to centromeres exclusively during early G1 phase and is subsequently maintained across mitotic divisions. Using SNAP-based fluorescent pulse labeling, we now demonstrate that cell cycle-restricted chromatin assembly at centromeres is unique to CENP-A nucleosomes and does not involve assembly of other H3 variants. Strikingly, stable retention is restricted to the CENP-A/H4 core of the nucleosome, which we find to outlast general chromatin across several cell divisions. We further show that cell cycle timing of CENP-A assembly is independent of centromeric DNA sequences and instead is mediated by the CENP-A targeting domain. Unexpectedly, this domain also induces stable transmission of centromeric nucleosomes, independent of the CENP-A deposition factor HJURP. This demonstrates that intrinsic properties of the CENP-A protein direct its cell cycle-restricted assembly and induces quantitative mitotic transmission of the CENP-A/H4 nucleosome core, ensuring long-term stability and epigenetic maintenance of centromere position.FCT fellpwships: (SFRH/BD/74284/2010, SFRH/BPD/69115/2010), National Institutes of Health grant: (GM082989), Burroughs Wellcome Fund (Career Award in the Biomedical Sciences), Rita Allen Foundation Scholar Award, Instituto Gulbenkian de Ciência, European Commission FP7 Program, EMBO

High Excitation Molecular Gas in the Magellanic Clouds

We present the first survey of submillimeter CO 4-3 emission in the Magellanic Clouds. The survey is comprised of 15 6'x6' maps obtained using the AST/RO telescope toward the molecular peaks of the Large and Small Magellanic Clouds. We have used these data to constrain the physical conditions in these objects, in particular their molecular gas density and temperature. We find that there are significant amounts of molecular gas associated with most of these molecular peaks, and that high molecular gas temperatures are pervasive throughout our sample. We discuss whether this may be due to the low metallicities and the associated dearth of gas coolants in the Clouds, and conclude that the present sample is insufficient to assert this effect.Comment: 18 pages, 3 figures, 5 tables. To appear in Ap

Better Nonlinear Models from Noisy Data: Attractors with Maximum Likelihood

A new approach to nonlinear modelling is presented which, by incorporating the global behaviour of the model, lifts shortcomings of both least squares and total least squares parameter estimates. Although ubiquitous in practice, a least squares approach is fundamentally flawed in that it assumes independent, normally distributed (IND) forecast errors: nonlinear models will not yield IND errors even if the noise is IND. A new cost function is obtained via the maximum likelihood principle; superior results are illustrated both for small data sets and infinitely long data streams.Comment: RevTex, 11 pages, 4 figure

Spin-splitting in the quantum Hall effect of disordered GaAs layers with strong overlap of the spin subbands

With minima in the diagonal conductance G_{xx} and in the absolute value of the derivative |dG_{xy}/dB| at the Hall conductance value G_{xy}=e^{2}/h, spin-splitting is observed in the quantum Hall effect of heavily Si-doped GaAs layers with low electron mobility 2000 cm^2/Vs in spite of the fact that the spin-splitting is much smaller than the level broadening. Experimental results can be explained in the frame of the scaling theory of the quantum Hall effect, applied independently to each of the two spin subbands.Comment: 4 pages, 4 figure

A retrospective analysis of the diagnostic performance of 11C-choline PET/CT for detection of hyperfunctioning parathyroid glands after prior negative or discordant imaging in primary hyperparathyroidism

BACKGROUND: Identifying the correct location of a parathyroid adenoma in patients with primary hyperparathyroidism (pHPT) is crucial as it can guide surgical treatment. This study aimed to determine the diagnostic performance of 11C-choline PET/CT in patients with pHPT as a next in-line scan after primary negative or discordant first-line imaging. METHODS: This was a retrospective single-center cohort study. All patients with pHPT that were scanned utilizing 11C-choline PET/CT, after prior negative or discordant imaging, between 2015 and 2019 and who subsequently underwent parathyroid surgery were included. The results of the 11C-choline PET/CT were evaluated lesion-based, with surgical exploration and histopathological examination as the gold standard. RESULTS: In total, 36 patients were included of which three patients were known to have Multiple Endocrine Neoplasia (MEN) syndrome. In these 36 patients, 40 lesions were identified on 11C-choline PET/CT and 37 parathyroid lesions were surgically removed. In 34/36 (94%) patients a focused parathyroidectomy was performed, in one patient a cervical exploration due to an ectopically identified adenoma, and in one patient a bilateral exploration was performed because of a double adenoma. Overall, per-lesion sensitivity of 11C-choline PET/CT was 97%, the positive predictive value was 95% and the accuracy was 94% for all parathyroid lesions. CONCLUSIONS: In patients with pHPT and prior negative or discordant first-line imaging results, pathological parathyroid glands can be localized by 11C-choline PET/CT with high sensitivity and accuracy

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images